Design of Experiments
Find cause-and-effect relationships
Why experimental design?
When collecting new data for multivariate modeling, one should pay attention to the following criteria:
- Efficiency: Get more information from fewer experiments
- Focusing: Collect only the information that is really needed
There are four basic ways to collect data for an analysis:
- Obtain historical data
- Collect new data
- Run specific experiments by disturbing (exciting) the system being studied
- Design experiments in a structured, mathematical way
With designed experiments there is a better possibility of testing the significance of the effects and the relevance of the whole model.
Experimental design (commonly referred to as DOE) is a useful complement to multivariate data analysis because it generates “structured” data tables, i.e. data tables that contain an important amount of structured variation. This underlying structure will then be used as a basis for multivariate modeling, which will guarantee stable and robust models.
More generally, careful sample selection increases the chances of extracting useful information from the data. When one has the possibility to actively perturb the system (experiment with the variables), these chances become even greater. The critical part is to decide which variables to change, the intervals for this variation, and the pattern of the experimental points.
Easy and advanced experiment designs made easy with Design-Expert®.
Design-Expert® from Stat-Ease is more than an ordinary Design of Experiment tool. It comes together with Unscrambler and provides a powerful package to help you optimise your product or process, such as:
- Two-level factorial screening designs: Identify the vital factors that affect your process or product so you can make breakthrough improvements
- General factorial studies: Discover the best combination of categorical factors, such as source versus type of raw material supply
- Response surface methods (RSM): Find the optimal process settings to achieve peak performance
- Mixture design techniques: Discover the ideal recipe for your product formulation
- Combinations of process factors, mixture components, and categorical factors: Mix your cake (with different ingredients) and bake it too!
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Book: An introduction to Multivariate Analysis.
All updated 6th edition of the best selling book on chemometrics and multivariate techniques, covering PLS, PCA, TOS, DoE and much more.